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Area of Science:

  • Pharmacology and Drug Development
  • Biomedical Informatics
  • Artificial Intelligence in Medicine

Background:

  • The US Food and Drug Administration (FDA) encourages using real-world data (RWD) to enhance drug development.
  • Real-world evidence (RWE) derived from RWD reflects actual clinical practice.
  • Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), is increasingly integrated into pharmaceutical research.

Purpose of the Study:

  • To conduct a rapid review of studies utilizing both AI and RWD in drug development over the past 20 years.
  • To provide an overview of current applications and trends in AI and RWD integration.
  • To identify research gaps and future opportunities in this interdisciplinary field.

Main Methods:

  • Rapid literature review methodology.
  • Analysis of studies published over the last two decades.
  • Synthesis of findings on AI and RWD applications in drug development.

Main Results:

  • The most prevalent applications identified were adverse event detection, clinical trial recruitment, and drug repurposing.
  • Significant growth in the combined use of AI and RWD was observed over the review period.
  • Key areas of AI and RWD synergy include enhancing data analysis and generating robust real-world evidence.

Conclusions:

  • The integration of AI and RWD offers powerful new strategies for drug development.
  • Future research should focus on addressing identified gaps to further leverage AI and RWD.
  • Continued advancements in AI and RWD utilization promise to accelerate therapeutic innovation and improve patient outcomes.